Solar power panels are comprised of panels of solar cells, also known as photovoltaic cells, which convert the sun's energy into electrical power. Like all electrical devices, solar panels are subject to arc faults, discharging of electricity between conductors. Arc faults can trigger electrical fires, damage expensive equipment, and can cause shocks to humans who touch the solar panel hardware. Therefore, detecting arc faults and removing the power source as quickly and efficiently as possible is necessary.
Some common methods to detect arc faults use microcontrollers to sample low-frequency content in photovoltaic strings digitally using analog to digital converters. However, using microcontrollers to directly sample the voltage or current inherently limits the frequency spectrum available for analysis to a region much lower than what would be ideal. Wider bandwidth analysis stands a better chance of more properly determining the true presence of an arc, particularly in the presence of noise from AC inverters. Solar power inverters generate a significant amount of noise when converting the DC output of the solar panels to AC power used by the power grid. The switching power electronics inside the power inverter generate large amounts of noise at the fundamental switching frequency at which it operates and harmonics thereof. The magnitude of this noise can exceed the amplitude of arcs thereby reducing the likelihood of detection in narrow bandwidth and increasing the probability of nuisance trips. Increasing the bandwidth can enable a more robust detection algorithm to be implemented where more data points are available to determine whether or not an arc is present. Arcs produce a specific pink noise spectrum that can be detected more accurately when analyzed over a wide frequency spectrum. While it may be possible to detect the presence of an arc in a narrower bandwidth, those methods will be more susceptible to other noise generating components especially if they are not constant. RF interference and radio communication especially spread spectrum could potentially cover the entire bandwidth of “narrow” band detection circuits. This could lead to nuisance trips or worse yet, failing to detect an arc when in the presence of other noise sources.
Measuring the energy spectral density over groups of frequency bands can be performed as a method to determine if the noise spectrum matches that of an arc event. By understanding the pink noise spectrum characteristic of arc events, a method can be implemented to determine an amplitude threshold that is evaluated as a function of frequency. While a fixed threshold may be acceptable over a narrow bandwidth, utilizing a threshold as a function of frequency allows the detection bandwidth to be usefully extended. If a fixed threshold were employed, there would be no effective method for correctly setting it. If it were configured to operate according to the lower frequency, the noise amplitude at higher frequencies would never cross the detection threshold.
An arc fault detection solution according to the present disclosure is implemented using a true analog solution that takes current measurements at each panel over a large frequency spectrum. The arc fault detection system uses a “high-speed” analog front end made up of low-cost, commercially-available components to mix and convert high-frequency signals down to a point where a relatively low-cost microcontroller can analyze the signals effectively. The method also comprises a software algorithm which is immune to strong spikes, tones, or carrier signals in the frequency spectrum because it implements a “hunting and searching” mode that ignores strong signals and seeks quiet “valleys” in the spectrum to perform a noise floor analysis.
The arc fault detection system uses a panel monitoring device that is installed on solar panels such that it is always inline and available to measure solar panel performance. The panel monitoring device performs continuous and automatic arc fault detection. The panel monitoring device comprises a switching device configurable to disconnect an output from the solar panel upon detection of an arc fault event. Logic resident on the panel monitoring device is configured to scan a frequency spectrum of the solar panel and log locations of a plurality of valleys in the spectrum, monitor the plurality of valleys to determine if the plurality of valleys rises above a threshold value, report a fault status when the plurality of valleys rises above the threshold value, and automatically disable the output of the solar panel upon the determination of a fault status. Because the arc detection device is paired with each panel individually, the device is in the current loop of an arc event in both the parallel and series conditions.
A method of the present disclosure comprises scanning a frequency spectrum of the solar panel and logging locations of a plurality of valleys in the spectrum. The plurality of valleys is monitored to determine if the plurality of valleys rises above a threshold value. If the plurality of valleys rises above the threshold value, an arc fault has occurred, and the output of the solar panel is automatically disabled. This threshold is not limited to a fixed amplitude across frequency, but rather is a threshold defined as a function of frequency.